import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase		,_UpperCAmelCase		,unittest.TestCase					):


			"""simple docstring"""






			__a    :					List[str]     					=		StableDiffusionInpaintPipeline
			__a    :					Tuple     					=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			__a    :					Optional[Any]     					=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__a    :					List[Any]     					=		frozenset(
			    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			__a    :					Union[str, Any]     					=		frozenset([]					)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[Any]:

							'''simple docstring'''


							torch.manual_seed(0		)
							__lowercase					     =				UNetaDConditionModel(
							    block_out_channels=(32, 64) ,  layers_per_block=2 ,  sample_size=32 ,  in_channels=9 ,  out_channels=4 ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,  cross_attention_dim=32 ,  attention_head_dim=(2, 4) ,  use_linear_projection=lowerCAmelCase__ ,  )
							__lowercase					     =				PNDMScheduler(skip_prk_steps=lowerCAmelCase__		)
							torch.manual_seed(0		)
							__lowercase					     =				AutoencoderKL(
							    block_out_channels=[32, 64] ,  in_channels=3 ,  out_channels=3 ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,  latent_channels=4 ,  sample_size=1_28 ,  )
							torch.manual_seed(0		)
							__lowercase					     =				CLIPTextConfig(
							    bos_token_id=0 ,  eos_token_id=2 ,  hidden_size=32 ,  intermediate_size=37 ,  layer_norm_eps=1E-05 ,  num_attention_heads=4 ,  num_hidden_layers=5 ,  pad_token_id=1 ,  vocab_size=10_00 ,  hidden_act='''gelu''' ,  projection_dim=5_12 ,  )
							__lowercase					     =				CLIPTextModel(lowerCAmelCase__		)
							__lowercase					     =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

							__lowercase					     =				{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=0		)	->   Dict:

							'''simple docstring'''


							__lowercase					     =				floats_tensor((1, 3, 32, 32) ,  rng=random.Random(lowerCAmelCase__		)		).to(lowerCAmelCase__		)
							__lowercase					     =				image.cpu().permute(0 ,  2 ,  3 ,  1		)[0]
							__lowercase					     =				Image.fromarray(np.uinta(lowerCAmelCase__		)		).convert('''RGB'''		).resize((64, 64)		)
							__lowercase					     =				Image.fromarray(np.uinta(image + 4		)		).convert('''RGB'''		).resize((64, 64)		)
							if str(lowerCAmelCase__		).startswith('''mps'''		):
											__lowercase					     =				torch.manual_seed(lowerCAmelCase__		)
							else:
											__lowercase					     =				torch.Generator(device=lowerCAmelCase__		).manual_seed(lowerCAmelCase__		)
							__lowercase					     =				{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': init_image,
							    '''mask_image''': mask_image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}
							return inputs





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							__lowercase					     =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							__lowercase					     =				self.get_dummy_components()
							__lowercase					     =				StableDiffusionInpaintPipeline(**lowerCAmelCase__		)
							__lowercase					     =				sd_pipe.to(lowerCAmelCase__		)
							sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__		)

							__lowercase					     =				self.get_dummy_inputs(lowerCAmelCase__		)
							__lowercase					     =				sd_pipe(**lowerCAmelCase__		).images
							__lowercase					     =				image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							__lowercase					     =				np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]		)

							assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							super().test_inference_batch_single_identical(expected_max_diff=3E-3		)




@slow
@require_torch_gpu
class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/sd2-inpaint/init_image.png'''		)
							__lowercase					     =				load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''		)
							__lowercase					     =				load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
							    '''/yellow_cat_sitting_on_a_park_bench.npy'''		)

							__lowercase					     =				'''stabilityai/stable-diffusion-2-inpainting'''
							__lowercase					     =				StableDiffusionInpaintPipeline.from_pretrained(lowerCAmelCase__ ,  safety_checker=lowerCAmelCase__		)
							pipe.to(lowerCAmelCase__		)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__		)
							pipe.enable_attention_slicing()

							__lowercase					     =				'''Face of a yellow cat, high resolution, sitting on a park bench'''

							__lowercase					     =				torch.manual_seed(0		)
							__lowercase					     =				pipe(
							    prompt=lowerCAmelCase__ ,  image=lowerCAmelCase__ ,  mask_image=lowerCAmelCase__ ,  generator=lowerCAmelCase__ ,  output_type='''np''' ,  )
							__lowercase					     =				output.images[0]

							assert image.shape == (5_12, 5_12, 3)
							assert np.abs(expected_image - image		).max() < 9E-3





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/sd2-inpaint/init_image.png'''		)
							__lowercase					     =				load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''		)
							__lowercase					     =				load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
							    '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''		)

							__lowercase					     =				'''stabilityai/stable-diffusion-2-inpainting'''
							__lowercase					     =				StableDiffusionInpaintPipeline.from_pretrained(
							    lowerCAmelCase__ ,  torch_dtype=torch.floataa ,  safety_checker=lowerCAmelCase__ ,  )
							pipe.to(lowerCAmelCase__		)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__		)
							pipe.enable_attention_slicing()

							__lowercase					     =				'''Face of a yellow cat, high resolution, sitting on a park bench'''

							__lowercase					     =				torch.manual_seed(0		)
							__lowercase					     =				pipe(
							    prompt=lowerCAmelCase__ ,  image=lowerCAmelCase__ ,  mask_image=lowerCAmelCase__ ,  generator=lowerCAmelCase__ ,  output_type='''np''' ,  )
							__lowercase					     =				output.images[0]

							assert image.shape == (5_12, 5_12, 3)
							assert np.abs(expected_image - image		).max() < 5E-1





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict:

							'''simple docstring'''


							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							__lowercase					     =				load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/sd2-inpaint/init_image.png'''		)
							__lowercase					     =				load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''		)

							__lowercase					     =				'''stabilityai/stable-diffusion-2-inpainting'''
							__lowercase					     =				PNDMScheduler.from_pretrained(lowerCAmelCase__ ,  subfolder='''scheduler'''		)
							__lowercase					     =				StableDiffusionInpaintPipeline.from_pretrained(
							    lowerCAmelCase__ ,  safety_checker=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__ ,  torch_dtype=torch.floataa ,  )
							pipe.to(lowerCAmelCase__		)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__		)
							pipe.enable_attention_slicing(1		)
							pipe.enable_sequential_cpu_offload()

							__lowercase					     =				'''Face of a yellow cat, high resolution, sitting on a park bench'''

							__lowercase					     =				torch.manual_seed(0		)
							__lowercase					     =				pipe(
							    prompt=lowerCAmelCase__ ,  image=lowerCAmelCase__ ,  mask_image=lowerCAmelCase__ ,  generator=lowerCAmelCase__ ,  num_inference_steps=2 ,  output_type='''np''' ,  )

							__lowercase					     =				torch.cuda.max_memory_allocated()
							# make sure that less than 2.65 GB is allocated
							assert mem_bytes < 2.65 * 10**9
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Optional[Any]         =    logging.get_logger(__name__)

__a    :	Optional[Any]         =    torch.device("""cpu""")





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowercase					     =				Image.open(requests.get(lowercase				,     stream=lowercase				).raw				)
				return im





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				if swiftformer_name == "swiftformer_xs":
								return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]				)

				elif swiftformer_name == "swiftformer_s":
								return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]				)

				elif swiftformer_name == "swiftformer_l1":
								return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]				)

				elif swiftformer_name == "swiftformer_l3":
								return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]				)





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				dct.pop(lowercase				)
				__lowercase					     =				val





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				[]
				for k in state_dict.keys():
								__lowercase					     =				k
								if ".pwconv" in k:
												__lowercase					     =				k_new.replace('''.pwconv'''				,     '''.point_wise_conv'''				)
								if ".dwconv" in k:
												__lowercase					     =				k_new.replace('''.dwconv'''				,     '''.depth_wise_conv'''				)
								if ".Proj." in k:
												__lowercase					     =				k_new.replace('''.Proj.'''				,     '''.proj.'''				)
								if "patch_embed" in k_new:
												__lowercase					     =				k_new.replace('''patch_embed'''				,     '''swiftformer.patch_embed.patch_embedding'''				)
								if "network" in k_new:
												__lowercase					     =				k_new.split('''.'''				)
												if ls[2].isdigit():
																__lowercase					     =				'''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]				)
												else:
																__lowercase					     =				k_new.replace('''network'''				,     '''swiftformer.encoder.network'''				)
								rename_keys.append((k, k_new)				)
				return rename_keys





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				SwiftFormerConfig()

				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				__lowercase					     =				1000
				__lowercase					     =				'''huggingface/label-files'''
				__lowercase					     =				'''imagenet-1k-id2label.json'''
				__lowercase					     =				json.load(open(hf_hub_download(lowercase				,     lowercase				,     repo_type='''dataset'''				)				,     '''r'''				)				)
				__lowercase					     =				{int(lowercase				): v for k, v in idalabel.items()}
				__lowercase					     =				idalabel
				__lowercase					     =				{v: k for k, v in idalabel.items()}

				# size of the architecture
				if swiftformer_name == "swiftformer_xs":
								__lowercase					     =				[3, 3, 6, 4]
								__lowercase					     =				[48, 56, 112, 220]

				elif swiftformer_name == "swiftformer_s":
								__lowercase					     =				[3, 3, 9, 6]
								__lowercase					     =				[48, 64, 168, 224]

				elif swiftformer_name == "swiftformer_l1":
								__lowercase					     =				[4, 3, 10, 5]
								__lowercase					     =				[48, 96, 192, 384]

				elif swiftformer_name == "swiftformer_l3":
								__lowercase					     =				[4, 4, 12, 6]
								__lowercase					     =				[64, 128, 320, 512]

				# load state_dict of original model, remove and rename some keys
				if original_ckpt:
								if original_ckpt.startswith('''https'''				):
												__lowercase					     =				torch.hub.load_state_dict_from_url(lowercase				,     map_location='''cpu'''				,     check_hash=lowercase				)
								else:
												__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)
				__lowercase					     =				checkpoint

				__lowercase					     =				create_rename_keys(lowercase				)
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(lowercase				,     lowercase				,     lowercase				)

				# load HuggingFace model
				__lowercase					     =				SwiftFormerForImageClassification(lowercase				).eval()
				hf_model.load_state_dict(lowercase				)

				# prepare test inputs
				__lowercase					     =				prepare_img()
				__lowercase					     =				ViTImageProcessor.from_pretrained('''preprocessor_config'''				)
				__lowercase					     =				processor(images=lowercase				,     return_tensors='''pt'''				)

				# compare outputs from both models
				__lowercase					     =				get_expected_output(lowercase				)
				__lowercase					     =				hf_model(inputs['''pixel_values''']				).logits

				assert hf_logits.shape == torch.Size([1, 1000]				)
				assert torch.allclose(hf_logits[0, 0:5]				,     lowercase				,     atol=1E-3				)

				Path(lowercase				).mkdir(exist_ok=lowercase				)
				print(F"Saving model {swiftformer_name} to {pytorch_dump_folder_path}"				)
				hf_model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--swiftformer_name""",
		    default="""swiftformer_xs""",
		    choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
		    type=str,
		    help="""Name of the SwiftFormer model you'd like to convert.""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""",
		    default="""./converted_outputs/""",
		    type=str,
		    help="""Path to the output PyTorch model directory.""",
		)
		parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

		__a    :	Optional[int]         =    parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)